Statistical Bioinformatics Lab

Dr. Wang's lab is centered around large-scale complex data sets in recent genomic and familial studies and around important biological questions that emerge from the analysis of these data. The team focuses on the development of methods and software for the accurate measurement of high-throughput genomic data.

PI: Wenyi Wang

Department of Bioinformatics and Computational Biology

Wenyi Wang (王文漪) is an associate professor at Department of Bioinformatics and Computational Biology and Department of Biostatistics, MD Anderson Cancer center. She works in the area of statistical methods for high-throughput genomic data, cancer risk assessment and Bayesian modeling.

Latest News

May 5, 2018

A huge endeavor of the Pan-Cancer Analysis Working Group (PCAWG) 11 leads to hopefully a tasteful read: Portraits of genetic intra-tumour heterogeneity and subclonal selection across cancer types. Download the biorxiv preprint here

May 5, 2018

Apr 29, 2018

Our first statistical modeling work for the Li-Fraumeni Syndrome (LFS) is acceptd by Journal of the American Statistical Association after a 3-year journey! Congrats everyone! You may download the arXiv preprint here

Apr 29, 2018

My TAMU collaborator Val Johnson and I are jointly recruiting postdocs in cancer bioinformatics through this 2-year training program at TAMU. Citizenship or green card required. Send us your CV if interested.

February 6, 2018

We developed a new software R package called Famdenovo: predicting the probability of de novo status of a germline mutation in familial diseases. You may download the R package here

December 10, 2017

Our TAMU collaborators Amir and Val's latest work on Bayesian variable selection for survival outcomes is on arXiv. Read the preprint here